About DNLA
An independent judgment layer for enterprise AI
DNLA doesn't compete with your development team, your integrator, or your model vendor. We exist to connect strategy, architecture, operations, and economics into a single, defensible verdict, for the people who have to answer for the system afterward.
Founder's word
Why I started DNLA
For more than 25 years I've worked in a world where a single mistake costs millions: first in defense and homeland security systems, then in telecom and mobile, and later as a senior project manager inside large organizations. I grew up inside the world of automation and advanced technology, back when nobody called it AI yet, they called it machine learning. I was here well before the wave of hype that came with LLMs. That experience taught me one principle most of the AI market prefers to ignore: whoever built your system cannot be the one who judges it.
I founded QAi because I watched companies invest millions in AI systems without a single independent party ever checking whether they were actually healthy. I'm not here to build anything for you, I don't sell tools, and I don't take a commission from any vendor. My role is one thing only: to bring you the real picture, even when it isn't comfortable. Sometimes that means fixing something. Sometimes it means stopping everything.
I'm here to be the adult in the room, before the mistake gets too expensive.
Yaron Genad · Founder & CEO, DNLA
Who this is for
Built to serve four audiences at once
Leadership & board
Understand exactly what money is at risk.
Customers
Know exactly what they're buying.
Investors
Understand whether they're looking at a differentiated, repeatable business.
Engineers
Know precisely how the audit is carried out in practice.
First principles
What we won't compromise on
Radical independence
Whoever builds the system cannot be its sole judge. The audit isn't tied to a vendor, a tool, or a model.
Surgical truth
The goal isn't to reassure the client; it's to reveal the system's actual state, even when the conclusion is to stop.
Operator before tool
A system's value depends on judgment, accountability, and business context, not model power alone.
Economics before technology
Every technical finding must translate into money: current cost, future cost, money at risk, cost to fix.
Scale economics before scale excitement
In AI systems, every new customer adds compute, tokens, retrieval, and monitoring cost. A healthy system doesn't eliminate that cost; it makes each new customer cost less than the last.
Measurement before trust
An AI system that isn't continuously measured isn't a managed system. It's a gamble.
Where we fit
DNLA doesn't operate in a vacuum
Organizations already lean on dev shops, integrators, AI consultants, internal data teams, observability tools, security advisors, and model vendors. Each covers part of the picture, and each often has a built-in interest in the project continuing. DNLA is the independent judgment function that connects all of it into one decision.
| Alternative | What it gives you | What's missing | Where DNLA fits in |
|---|---|---|---|
| Dev shop / integrator | Builds, implements, and integrates the system. | Not always the right party to judge whether to continue, change course, or stop. | Delivers an independent opinion on quality, risk, and whether continuing is worth it. |
| General AI consultant | Strategy, ideas, use-case selection, general direction. | Often lacks engineering depth, system-level testing, and translation into money at risk. | Connects strategy, architecture, operations, and economics into one verdict. |
| Internal team | Deep familiarity with the organization and the system. | Can be captive to assumptions, internal politics, or sunk cost. | Brings an outside view, unthreatened by an uncomfortable conclusion. |
| Observability / monitoring tool | Data, logs, metrics, alerts. | Doesn't replace business judgment, Problem Fit, or investment decisions. | Interprets the data into a Healthy / Tune / Fix / Rebuild / Kill decision. |
| Security or privacy consultant | Security, privacy, and compliance testing. | Doesn't necessarily assess AI architecture, data pipelines, evals, or scale economics. | Folds security and compliance risk into the overall system-health picture. |
| Model or tool vendor | Models, infrastructure, an API, or a platform. | Rarely neutral on whether its own tool is the right choice. | Assesses problem fit with no loyalty to any tool, model, or vendor. |
Want the outside view on your AI system?
Tell us where you are and we'll point you to the right starting package.